0. Pre-Tutorial Exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Pre-Tutorial Exercises\n",
    "\n",
    "If you've arrived early for the tutorial, please feel free to attempt the following exercises to warm-up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. Basic Python data structures\n",
    "# I have a list of dictionaries as such:\n",
    "\n",
    "names = [{'name': 'Eric',\n",
    "          'surname': 'Ma'},\n",
    "         {'name': 'Jeffrey',\n",
    "          'surname': 'Elmer'},\n",
    "         {'name': 'Mike',\n",
    "          'surname': 'Lee'},\n",
    "         {'name': 'Jennifer',\n",
    "          'surname': 'Elmer'}]\n",
    "\n",
    "# Write a function that takes in a list of dictionaries and a query surname, \n",
    "# and searches it for all individuals with a given surname.\n",
    "\n",
    "def find_persons_with_surname(persons, query_surname):\n",
    "    # Assert that the persons parameter is a list. \n",
    "    # This is a good defensive programming practice.\n",
    "    assert isinstance(persons, list)   \n",
    "    \n",
    "    results = []\n",
    "    for name in names:\n",
    "        if name['surname'] == query_surname:\n",
    "            results.append(name)\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Test your result below.\n",
    "results = find_persons_with_surname(names, 'Lee')\n",
    "assert len(results) == 1\n",
    "\n",
    "results = find_persons_with_surname(names, 'Elmer')\n",
    "assert len(results) == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-89c1f093e9b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfind_persons_with_surname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Ma'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "results = find_persons_with_surname(names, 'Ma')\n",
    "assert len(results) == 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# https://www.tutorialspoint.com/python/assertions_in_python.htm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 4, 2, 4, 1, 5, 4, 3, 4, 2, 1, 4, 1, 3, 4, 0, 1, 2, 4, 0, 4, 1, 0, 3, 0, 4, 4, 4, 2, 0, 3, 2, 4, 1, 2, 2, 0, 4, 0, 1, 4, 1, 2, 5, 1, 4, 2, 2, 5, 1, 3, 1, 4, 3, 5, 2, 0, 1, 4, 1, 3, 0, 1, 1, 3, 2, 3, 5, 5, 5, 4, 3, 3, 2, 0, 0, 5, 1, 0, 0, 0, 4, 1, 1, 3, 5, 3, 4, 5, 5, 2, 2, 4, 0, 2, 1, 0, 0, 2, 5]\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "lst = list()\n",
    "#random.seed(9001)\n",
    "for i in range(100):\n",
    "    #temp = random.randint(0,10000)\n",
    "    #random.seed(temp)\n",
    "    lst.append(random.randint(0,5))\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lst = list()\n",
    "for i in range(20):\n",
    "    lst.append(random.uniform(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7525874388092904, 0.9592356789130781, 0.2634430571309443, 0.006256586106564432, 0.07512778474837056, 0.6292901645822183, 0.8503088859834467, 0.9581808562837656, 0.6662849523140008, 0.2296232973486816, 0.018280048391809856, 0.9789996381731564, 0.8670347282231149, 0.7802258855370047, 0.2818568839659219, 0.27084522698031666, 0.6595103467375983, 0.7546670749704983, 0.604656791352812, 0.3504964357719832]\n"
     ]
    }
   ],
   "source": [
    "print(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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bRktKSACNJnRHIaEHGkko3+rVsGqVzctKfllavJjVkFBXZ2F569bQlVTHs8/a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9186a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(20),lst)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.linspace(0,1, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A6BILAECXWAAAusQCANAlFgCALrEAAHSJBQCgSywAAF1iAQDoEgsAQJdYAAC6xAIA0CUW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\nAOgSCwBA1/8DBAe+bZJxNlwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2eb860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,lst,'w', label='Ratings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bRktKSACNJnRHIaEHGkko3+rVsGqVzctKfllavJjVkFBXZ2F569bQlVTHs8/a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa55fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(20),lst)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}